import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import numpy as np
import plotly.io as pio
pio.renderers.default ='notebook'

Lionel Messi Data Analysis

Lionel Andres Messi started his career in 2003, scoring a total of 701 goals playing for different Clubs, not counting the Argentinian National Team. The Follow dataset analyses and presents that information along different charts and graphs.

df = pd.read_csv('messi.csv')

df['Date'] = pd.to_datetime(df['Date'])
df['Goles'] = 1

How many goals has Messi scored along different tournaments

uno = df.groupby(['Tournament']).count()

colors = px.colors.cyclical.Phase
fig = px.pie(df, names='Tournament', values='Goles', height=800, width=1020,color_discrete_sequence= colors)
fig.update_traces(hoverinfo='label+percent', textinfo='value', textfont_size=15,
                  marker=dict(colors=colors, line=dict(color='#000000', width=1)))
fig.update_layout(uniformtext_minsize=12, uniformtext_mode='hide' ,title_text='Goals Scored by Tournament',title_x=0.45)
fig.show()

color = px.colors.qualitative.Set1
fig = px.histogram(df,y=df.Goles.sort_values(), x='Tournament', height=800, width=1020, barmode='relative',text_auto  = True,title='Goals scored by Tournament',color='Tournament',
color_discrete_sequence= color).update_xaxes(categoryorder="total descending")
fig.show()

# minmatch = pd.DataFrame()
# minmatch['Tournament'] = df2['Tournament'] 
# minmatch['MinuteNum2'] = df2['MinuteNum2']

# ser = df2.groupby(['Tournament','MinuteNum2']).Goles.count()
# ser = ser.unstack().fillna(0).astype('int')

df2 = df.copy()

df2['MinuteNum'] = df2['Minute']

df2['MinuteNum'] = df2['MinuteNum'].str.replace('[^0-9]', '',regex=True).astype('int64')

df2['MinuteNum2'] = df2['MinuteNum'] 
df2.loc[df2['MinuteNum2'] > 90, 'MinuteNum2'] = 93

Minute of the game when Messi scored all his 701 goals

ordernumb = df2.groupby(['MinuteNum2'])['Tournament'].count().to_frame().reset_index()
ordernumb = ordernumb.rename(columns={'MinuteNum2': 'Minute', 'Tournament': 'Goals Scored'})
ordernumb2 = ordernumb.copy()
ordernumb2

	Minute	Goals Scored
0	2	1
1	3	4
2	4	4
3	5	8
4	6	2
...	...	...
85	87	13
86	88	9
87	89	9
88	90	12
89	93	33

90 rows × 2 columns

fig = px.bar(ordernumb2, x='Minute', y= 'Goals Scored', height=600, width=1020, title='Minute of the game when Messi scored' \
        ,color='Goals Scored', text='Goals Scored')
fig.update_traces(texttemplate='%{text:.1.5s}', textposition='outside')
# fig.update_layout(uniformtext_minsize=8, uniformtext_mode='hide')
fig.show()

The data shows that during his career, he has scored more goals (14) at minute 55', then at minute 78' and 87' he has 13, being the second and the third largest amount 87'. All the goals converted during the extra time have been summed and totalized aside, with a total of 33.

BoxPlot showing mean, median and quarter in minutes when Messi scores (By Tournament)

color = px.colors.qualitative.Set1
fig = px.box(df2, y='MinuteNum2', x="Tournament",color="Tournament", color_discrete_sequence= color, boxmode= "overlay",height=600, width=1020 )
fig.show()

The graph shown before indicates the mean, median and quarter when Messi has scored all his goals, divided also by Tournament.

Which team has received most of Messis Goals (Top 25)

rival = df2.groupby(['Opponent'])['Goles'].count().sort_values(ascending=False).to_frame('Total').head(25).reset_index()
rival

	Opponent	Total
0	Sevilla FC	38
1	Atlético de Madrid	32
2	Valencia CF	31
3	Athletic Bilbao	29
4	Real Betis Balompié	26
5	Real Madrid	26
6	CA Osasuna	25
7	RCD Espanyol Barcelona	25
8	Levante UD	24
9	Getafe CF	21
10	Deportivo de La Coruña	20
11	SD Eibar	20
12	Rayo Vallecano	18
13	Real Sociedad	18
14	RCD Mallorca	16
15	Villarreal CF	16
16	Granada CF	15
17	Real Zaragoza	14
18	Celta de Vigo	14
19	Málaga CF	13
20	UD Almería	13
21	Deportivo Alavés	13
22	Racing Santander	12
23	CD Leganés	11
24	Arsenal FC	9

fig = px.bar(rival,height=800, width=1020,y='Total', x='Opponent',color='Opponent', color_continuous_scale=px.colors.qualitative.Set2, text='Total', title='Which team has received most of Messis Goals (Top 25)')
fig.update_traces(texttemplate='%{text:1.3.s}', textposition='outside')
fig.update_xaxes(tickangle=45)
fig.show()

Sevilla, Atletico de Madrid and Valencia FC were the teams more affected by Messi with a total of 38, 32 and 31 goals suffered by the argentinian striker

Amount of goals scored during Messi's career (Total and by Tournament)

df2['Date'] = df2['Date'].apply(pd.Timestamp)
df2['Date_M'] = df2['Date'].apply(pd.Timestamp)
df2['Date_M'] = pd.to_datetime(df2['Date_M']).dt.to_period('M')
df2['Date_Y'] = df2['Date'].dt.year

years = df2.groupby(['Date_Y'])['Goles'].count().to_frame('Total').reset_index()

fig = px.line(years, x="Date_Y", y='Total', text="Total",height=700, width=1100, title='All Goals scored by Messi during his Career (Total)')
fig.update_traces(textposition="bottom right")
fig.update_layout(
   xaxis = dict(
      tickmode = 'linear',
      tick0 = 1 
   )
)
fig.show()

During the period of 2003 to 2022, the year 2012 was the best for Leo Messi, scoring a total of 79 goals just playing for Barcelona, not taking in consideration his performance at the Argentinean National Team.

years2 = df2.groupby(['Date_Y','Tournament'])['Goles'].count().to_frame('Total').reset_index()
years2

	Date_Y	Tournament	Total
0	2004	2ª B - Grupo III	5
1	2005	2ª B - Grupo III	1
2	2005	Champions League	1
3	2005	LaLiga	2
4	2006	Champions League	1
...	...	...	...
64	2021	LaLiga	23
65	2021	Ligue 1	1
66	2022	Champions League	4
67	2022	Ligue 1	12
68	2022	Trophée des Champions	1

69 rows × 3 columns

fig = px.line(years2, x="Date_Y", y='Total', text="Total",height=700, width=1100,color='Tournament',markers=True, title='Goals scored by Messi during his Career (By Tournament)')
fig.update_traces(textposition="bottom right")
fig.update_layout(
   xaxis = dict(
      tickmode = 'linear',
      tick0 = 1 
   )
)
fig.show()